Research on hybrid optimization and deep learning modeling method in the performance seeking control

Abstract

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

Research on aero-engine performance seeking control based on the NN-PSM on-board model

“…As for the accuracy of the on-board model, it not only influences the optimization results but may also bring the engine to unsatisfied constraints. 9,10 At present, most scholars use the piecewise linear model (PLM) or component level model (CLM) as the on-board model for PSC. [11][12][13][14][15][16][17] The PLM has good realtime performance.…”

Research on performance seeking control based on Beetle Antennae Search algorithm

“…5,6 However, the control error is inevitably existing if the engine model is linearized due to the strong nonlinear characteristic of engine. Therefore, some scholars proposed a series of PSC optimization algorithms, [7][8][9][10][11][12][13][14][15][16][17] such as MAPS (Model-Assisted Pattern Search), 18 SQP (Sequential Quadratic Programming), 19,20 FSQP, 7 PSMA (Particle Self-Migrating Algorithm), 8,9 GA (Genetic Algorithm), 10 PSO (particle swarm optimization), 11 IA (Interval Analysis). 12 During these algorithms, the probability-based algorithms, such as GA, PSO, make engine get better engine performance.…”